BNNG Algorithm Modeling for Vehicle Classification Recognition under Non Line-of -sight Environment

Using video in traffic monitoring is one of the most active research domains in the computer vision community. TrafficMonitor, a system that employs a hybrid approach for automatic vehicle tracking and classification on highways using a simple stationary calibrated camera, is presented. The proposed system consists of three modules: vehicle detection, vehicle tracking, and vehicle classification. Moving vehicles are detected by an enhanced Gaussian mixture model background estimation algorithm. The design includes a technique to resolve the occlusion problem by using a combination of two-dimensional proximity tracking algorithm and the Kanade–Lucas–Tomasi feature tracking algorithm. The last module classifies the shapes identified into five vehicle categories: motorcycle, car, van, bus, and truck by using three-dimensional templates and an algorithm based on histogram of oriented gradients and the support vector machine classifier. Several experiments have been performed using both real and simulated traffic in order to validate the system. The experiments were conducted on GRAM-RTM dataset and a proper real video dataset which is made publicly available as part of this work.

This paper describes the 1990 test of an automatic vehicle classification (AVC) system employing an inductance detector loop and photoelectric sensors emitting infrared-spectrum light beams, which were reflected off commonly used raised traffic lane markers. The passing of a vehicle activated the sensors. Signals from the activation go through a special microprocessor into a personal computer for interpretation, display, and recording. Vehicle speed and axle patterns are inferred from the pattern of the photoelectric beam interruptions. The system is tested several times at a high-speed (50–65 mph) Oklahoma turnpike site by comparing the automatic classifications with classifications by a person. The researchers use computer printouts and written field observations to evaluate the number and type of errors. An error is said to occur when the automatic vehicle classification does not agree with the classification by the observer. The evaluation shows that the first-generation AVC system correctly classifies about 95% of 1,736 observed vehicles. Further analysis of the raw data and the errors found therein suggests that, with modifications, the AVC system can correctly classify about 97–98% of the vehicles. Snow or heavy rain does not occur during the tests, so ability to classify in these conditions is not evaluated.

The establishment of a comprehensive statewide traffic counting program is discussed. The program comprises automatic traffic recorder (ATR), automatic vehicle classification (AVC), and weigh-in-motion (WIM) sites for the state of Delaware. The program was undertaken to review, establish, and implement effective statistical and procedural methods. The second phase of a two phase project, which implements the methodologies that were derived in the first phase, is presented. Using descriptive analysis and seasonal grouping, the number and location of sites needed for each of the three types of traffic monitoring devices were determined. Existing field data from Delaware's current ATR locations allowed for a statistical determination of the necessary number and road-type group distribution for the ATR sites. The absence of field data for AVC and WIM sites, however, necessitated alternative methods for determining the number and location of the traffic monitoring devices. As a result, a combination of statistical analysis and engineering judgment must be used for the establishment of any statewide traffic monitoring system.

Alternative Automatic Vehicle Classification Method The paper deals with the new method of automatic vehicle classification called ALT (ALTernative). Its characteristic feature is versatility resulting from its open structure, moreover a user can adjust the number of vehicles and their category according to individual requirements. It uses an algorithm for automatic vehicle recognition employing data fusion methods and fuzzy sets. High effectiveness of classification while retaining high selectivity of division was proved by test results. The effectiveness of classification of all vehicles at the level of 95% and goods trucks of 100% is more than satisfactory.

The following paper depicts an architectural layout and operational insights towards an optoelectronic system dedicated for vehicle counting and classification, designed to monitor occupancy of rest areas. The system is based upon an image cue acquisition by video cameras and advanced signal processing mechanism. In order to satisfy the appropriate realization of measurement procedure (counting and classification), a specific measurement configuration scheme alongside optimized video stream capturing parameters were set up. Concerning signal processing methods, the most effective and appropriate for the purpose of vehicle counting and classification were selected. Moreover, the article presents the outcomes of the designed measurement campaign conducted on the S7 expressway within the agenda of the National Center for Research and Development RID project. The results obtained with use of the designed methodology were compared to the outcomes of the manual method.

This paper presents a kind of automatic vehicle classification (AVC) instrument for expressway toll collection system based on multiple sensor information fusion technique according to the complicated characteristics of vehicle types in China, In order to develop the instrument, we make use of the video detection segregator, infrared detection technique, piezomagnetic sensor, multiple sensor information fusion technique based on BP neural network. The training result is obtained by 1500 training samples and the training accuracy rate is up to 99.4%. And the simulation accuracy rate on 500 samples is up to 99.2%. The results show that the classification precision is high and the instrument has great value to be popularized.

Artificial intelligence through deep neural networks is now widely used in a variety of applications that have profoundly altered human livelihoods in a variety of ways. People's daily lives have become much more convenient. Image recognition, smart recommendations, self-driving vehicles, voice translation, and a slew of other neural network innovations have had a lot of success in their respective fields. The authors present the ANN applied in weather forecasting. The prediction technique relies solely upon learning previous input values from intervals in order to forecast future values. And also, Convolutional Neural Networks (CNNs) are a form of deep learning technique that can help classify, recognize, and predict trends in climate change and environmental data. However, due to the inherent difficulties of such results, which are often independently identified, non-stationary, and unstable CNN algorithms should be built and tested with each dataset and system separately. On the other hand, to eradicate error and provides us with data that is virtually identical to the real value we need Artificial Neural Networks (ANN) algorithms or benefit from it. The presented CNN model's forecasting efficiency was compared to some state-of-the-art ANN algorithms. The analysis shows that weather prediction applications become more efficient when using ANN algorithms because it is really easy to put into practice.

Problems related to determining traffic noise characteristics are discussed in the context of automatic dynamic noise analysis based on noise level measurements and traffic prediction models. The obtained analytical results provide the second goal of the study, namely automatic vehicle counting and classification. Several traffic prediction models are presented and compared to the results of in-situ noise level measurements. Synchronized audio recordings were made to determine Sound Quality parameters describing the nature of acquired sound signals. Video recordings and information about the traffic structure using commercially available automatic vehicle detection methods were also collected in order to create ground truth data used for the experiments.

According to Vision Indonesia, data on people with eye diseases in Indonesia in 2018-2019 were 3 million people or about 1.5% of the total population. So far, public information or knowledge about the recognition of eye disease disorders is still lacking. The problem in this study is how to educate the public about the introduction of eye diseases based on information on symptoms of the disease and how to apply the web-based Artificial Neural Network (ANN) algorithm for the introduction of eye diseases. The ANN algorithm in the eye disease recognition education system can conclude knowledge even though it does not have certainty and takes it into account sequentially so that the process is faster. In terms of educational content about eye disease recognition, this is a novelty to use. This research aims to create an educational system for introducing eye diseases based on information on symptoms of the disease and applying a web-based Artificial Neural Network (ANN) algorithm for the recognition of eye diseases. The method used is the Artificial Neural Network algorithm method. The work of ANN in the education system for the introduction of eye diseases is to make parameters of eye disease symptoms or indicators that will produce the type of eye disease. The research material used is data on types of eye diseases and symptoms of each type of eye disease. The research results are to create an education system that can help the public recognise eye diseases based on the symptoms of these eye diseases that can be run on a web platform. The Artificial Neural Network (ANN) algorithm can manage input analysis data from disease indicators and show the initial results of eye diseases that can be detected. suffered by someone based on Training Results Weights and Bias v11= 1.6769, v01= 0.4356, w11= -1.5233, w01= 0.3242. Based on white box testing, the test results are free from logical errors. The results of this study indicate that the use of the ANN algorithm for eye disease recognition shows accurate results based on eye disease symptom data.

We apply and compare various Artificial Neural Network (ANN) and other algorithms for automatic morphological classification of galaxies. The ANNs are presented here mathematically, as non-linear extensions of conventional statistical methods in Astronomy. The methods are illustrated using different subsets Artificial Neural Network (ANN) and other algorithms for automatic morphological classification of galaxies. The ANNs are presented here mathematically, as non-linear extensions of conventional statistical methods in Astronomy. The methods are illustrated using different subsets from the ESO-LV catalogue, for which both machine parameters and human classification are available. The main methods we explore are: (i) Principal Component Analysis (PCA) which tells how independent and informative the input parameters are. (ii) Encoder Neural Network which allows us to find both linear (PCA-like) and non-linear combinations of the input, illustrating an example of unsupervised ANN. (iii) Supervised ANN (using the Backpropagation or Quasi-Newton algorithms) based on a training set for which the human classification is known. Here the output for previously unclassified galaxies can be interpreted as either a continuous (analog) output (e.g. $T$-type) or a Bayesian {\it a posteriori} probability for each class. Although the ESO-LV parameters are sub-optimal, the success of the ANN in reproducing the human classification is 2 $T$-type units, similar to the degree of agreement between two human experts who classify the same galaxy images on plate material. We also examine the aspects of ANN configurations, reproducibility, scaling of input parameters and redshift information.

Vehicle Classification Method Based on Single-Point Magnetic Sensor

The computational cost of training artificial neural network (ANN) algorithms limits the use of large systems capable of processing complex problems. Implementing ANNs on a parallel or distributed platform to improve performance is therefore desirable. This work illustrates a method to predict and evaluate the performance of distributed ANN algorithms by analyzing the performance of the comparatively simple mathematical operations, which are used to construct the ANN. The ANN algorithms are divided into simple components: matrix and vector multiplication, matrix processed through a function, competition in a matrix. These basic operational parts are examined individually and it is demonstrated that the computation processes of distributed neural networks can be derived from the composition of these basic operations. Three popular network architectures are examined: multi-layer perceptrons with back-propagation learning, self-organizing map, and radial basis functions network.

Australia's cities face increasing concerns about traffic congestion and pollution, concerns that encourage an interest in sustainable transport choices. Most of the focus of these concerns has been on cars, public transport, and active transport, whereas the role of powered two-wheelers (PTWs)–-specifically, motorcycles and motor scooters–-has received little attention. Most PTW research focuses on road safety implications, and little is known about their usage patterns. Automatic vehicle identification counts at two major tolled freeways in Melbourne, Victoria, Australia–-one radial freeway close to the city center and one orbital freeway 20 to 30 km southeast of the city center–-were analyzed. Automatic vehicle classification equipment recorded hourly traffic counts and PTW counts from 2006 to 2012. The classified count data were analyzed to explore the pattern of PTW use by time of day, by day of week, and across the seasons. Regression analyses were used to examine weather effects. Comparisons were drawn between PTW and non-PTW traffic and between the radial and orbital freeways. A discussion of the findings and suggestions for additional research are presented.

This study analyzed consumer intention to repurchase a smartphone, using an analysis of previous research and suggesting this as a research model. To this end, factors influencing “intention to repurchase” the smartphone were derived as social influence, consumer satisfaction, emotional loyalty, and habit. In addition, statistical analysis was conducted to investigate consumer repurchase intentions and the causal relationships. It is also possible to investigate whether consumer habits are linked to repurchases by analyzing their psychological inclinations; therefore, in this study, 390 people who repurchased a smartphone over the past two years were surveyed, and data were collected. This study analyzed the causal relationships among the factors using SPSS 24.0. Moreover, the causal relationship analysis was enhanced using the artificial neural network (ANN) algorithm. With multiple regression analysis and the ANN algorithm, consumer satisfaction (0.71), emotional loyalty (0.108), and social influence (0.062) were determined to affect the intention to repurchase. This means that the ANN algorithm can be used over multiple regression analysis and improve the results of the analysis. In addition, this study also provided practitioners with a way to improve their understanding of consumer behavior intention to repurchase smartphones.

This paper presents an algorithm for reducing speckle noise from optical coherence tomography (OCT) images using an artificial neural network (ANN) algorithm. The noise is modeled using Rayleigh distribution with a noise parameter, sigma, estimated by the ANN. The input to the ANN is a set of intensity and wavelet features computed from the image to be processed, and the output is an estimated sigma value. This is then used along with a numerical method to solve the inverse Rayleigh function to reduce the noise in the image. The algorithm is tested successfully on OCT images of Drosophila larvae. It is demonstrated that the signal-to-noise ratio and the contrast-to-noise ratio of the processed images are increased by the application of the ANN algorithm in comparison with the respective values of the original images.